code: def regression(self):
      from sklearn.metrics import (
          mean_absolute_error,
          mean_squared_error,
      )
  
      true, pred = torch.cat(self._true), torch.cat(self._pred)
      return {
          "mae": float(
              round(mean_absolute_error(true, pred), cfg.round)
          ),
          "mse": float(
              round(mean_squared_error(true, pred), cfg.round)
          ),
          "rmse": float(
              round(
                  math.sqrt(mean_squared_error(true, pred)), cfg.round
              )
          ),
      }
  
imports: import logging
  import math
  import sys
  import torch
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.utils.device import get_current_gpu_usage
  from graphgym.utils.io import dict_to_json, dict_to_tb, makedirs
label_desc: Use image node_id 10 for calling the Logger obj's underlying member method code with example usage: obj.regression() and returns: dict